UNCONDITIONAL CONVERGENCE IN MANUFACTURING*
Dani Rodrik
Unlike economies as a whole, manufacturing industries exhibit strong unconditional convergence in labor productivity. The article documents this atvarious levels of disaggregation for a large sample covering more than 100
countries over recent decades. The result is highly robust to changes in thesample and specification. The coefficient of unconditional convergence is estimated quite precisely and is large, at between 2–3% in most specifications and2.9% a year in the baseline specification covering 118 countries. The article also
finds substantial sigma-convergence at the two-digit level for a smaller sample
of countries. Despite strong convergence within manufacturing, aggregate convergence fails due to the small share of manufacturing employment in
low-income countries and the slow pace of industrialization. Because of data
coverage, these findings should be as viewed as applying to the organized,
formal parts of manufacturing. JEL Codes: O40, O14.

I. Introduction
Neoclassical growth theory establishes a presumption thatcountries with access to identical technologies should converge
to a common income level. Countries that are poorer and havehigher marginal productivity of capital should grow more rapidly
in the transition to the long-run steady state. In an open global
economy, access to foreign capital and foreign markets (whichremoves finance and market size as constraints) further strengthens the presumption of convergence.
However, empirical work has not been kind to this proposition. Selected developing countries, such as those in East Asia,
have grown quickly. But when poor countries are taken as awhole, there is no systematic tendency for them to grow fasterthan rich ones, over any reasonably long time horizon for which
we have data.1 Whatever convergence one can find is conditional:
It depends on policies, institutions, and other country-specific circumstances. The only clear-cut exceptions to the rule seem to be*This is a substantially revised version of Rodrik (2011a). I am grateful to
UNIDO for making the INDSTAT2 and INDSTAT4 databases available. I also
thank Cynthia Balloch for research assistance; the Weatherhead Center forInternational Affairs and the Center for International Development at Harvard
University for financial assistance; and Alberto Abadie, Daron Acemoglu,
Jonathan Temple, three referees, and the editors for useful suggestions.
1. On convergence in the decade before the global financial crisis of 2008–2009,see Subramanian (2011, chap. 4), and Rodrik (2011b).
! The Author(s) 2012. Published by Oxford University Press, on behalf of President andFellows of Harvard College. All rights reserved. For Permissions, please email: journals.permissions@oup.com
The Quarterly Journal of Economics (2013), 165–204. doi:10.1093/qje/qjs047.
Advance Access publication on November 18, 2012.

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states/regions within a unified economy such as the United States(Barro and Sala-i-Martin 1991).2
If growth rates are characterized by conditional instead of
unconditional convergence, economies will tend toward different
levels of income in the long run. Lack of empirical support for(unconditional) convergence has led theory in the direction ofmodels with endogenous technological change, which do notnecessarily exhibit convergence, and to empirical work that
focuses on identifying the conditioning variables that makes convergence feasible (see Acemoglu 2009 on theory, and Durlauf,
Johnson, and Temple 2005 on empirical work).
In contrast to this large literature, I show in this article that
unconditional convergence does exist, but it occurs in the modern
parts of the economy rather than the economy as a whole. In
particular, I document a highly robust tendency toward convergence in labor productivity in manufacturing activities, regardless of geography, policies, or other country-level influences. The
coefficient of unconditional convergence (beta) is large—2.9% a
year in my baseline specification that covers 118 countries—andestimated quite precisely, with more disaggregated specifications
generally yielding somewhat higher estimates. A convergence
rate of 2.9% implies that industries that are, say, a tenth of theway to the technology frontier (roughly the bottom 20% of theindustries in the sample) experience a convergence boost in
their labor productivity growth of 6.7 percentage points perannum (0.029  ln(10)).
Figures I, II, and III illustrate the central result of this article and place it in proper perspective. Each figure shows the relationship between labor productivity in some base period (on thevertical axis) and its growth rate over the subsequent decade,controlling for period-specific influences. Figure I, where each
dot stands for a particular country in a specific decade, presentsa typical nonconvergence result for country-level productivity.
There is no systematic tendency for countries that start withlower productivity (measured here by gross domestic product[GDP] per worker) to grow more rapidly.

2. Some studies also find unconditional convergence among the richer
Organisation for Economic Co-operation and Development (OECD) countries,
but it is difficult to know what to make of this result in light of the obvious
sample selection bias (Baumol 1986; DeLong 1988).

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FIGURE I
Lack of Convergence in Economy-wide Labor Productivity
Variable on the vertical axis is growth of GDP per worker over four separate decades (1965–1975, 1975–1985, 1985–1995, 1995–2005), controlling for
decadal fixed effects. Source of data: PWT 7.0. Sample is restricted to countries
included in the manufacturing convergence regressions.

Figure II depicts the analogous relationship for individual
manufacturing industries. Each dot on this scatter plot represents a two-digit industry in a specific country. (Illustrative
industries: food and beverages, chemicals and chemical products,
motor vehicles.) Each country enters Figure II with multiple
industries (but over a single time horizon, with the most recent
decade for which it has data). Period and industry-specific influences are controlled using industry, decade, and industry  decade fixed effects. Because there are no controls for country-level
determinants, Figure II represents a test of unconditional convergence similar in spirit to Figure I. (The need for period andindustry fixed effects are motivated subsequently.) The negativeand highly significant slope is unmistakable, illustrating the central conclusion: Manufacturing exhibits a strong tendency for
unconditional convergence. Industries that start at lower levelsof labor productivity experience more rapid growth in labor

168

QUARTERLY JOURNAL OF ECONOMICSFIGURE II
Unconditional Unconditionce in 2-digit Manufacturing Sectors
Variable on the vertical axis is the growth of value added per worker
in 2-digit manufacturing industries, controlling for period, industry, andperiod  industry fixed effects, where for each country the latest decade overwhich data are available is included. Source of data: INDSTAT2. For further
details on data and methods, see text.

productivity. As I show later, when country fixed effects areincluded the slope becomes even steeper. Conditional convergence is more rapid than unconditional convergence. But whatis striking in Figure II is the evident strength of convergence inthe data even in the absence of such controls. Figure III shows thesame convergence result for manufacturing in aggregate, withlabels identifying each country.3
My focus in this article is on what it is called
beta-convergence, where beta refers to the slope of the relationship in Figures I–III. I also present evidence for significant
3. There are some apparent anomalies in Figure III arising from compositional
differences across countries. For example, Suriname (SUR) shows up as the country
with the highest labor productivity. This is due to the fact that industry in thiscountry consists almost entirely of alumina and aluminum production, which ishighly capital- and energy-intensive. Such compositional issues provide an important rationale for working with more disaggregated data.

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FIGURE III
Unconditional Convergence in Aggregate Manufacturing
Variable on the vertical axis is the growth of value added per worker inaggregate manufacturing controlling for period fixed effects, where for eachcountry the latest decade over which data are available is included. Source of
data: INDSTAT2. For further details on data and methods, see text.

sigma-convergence, referring to convergence in productivity
levels, at least for recent time periods. Even if beta-convergenceholds, countries may fail to converge in levels as long as random
shocks to the growth process are relatively large.4 In the present
sample of industries, beta- and sigma-convergence go together. I
find that productivity dispersion was sharply reduced acrosscountries during 1995–2005 in the majority of two-digit manufacturing industries. In manufacturing taken as a whole, sigma
shrunk by 10 log-points during this period in the sample of 63
countries for which comparison is possible—a reduction of theorder of 10%.
4. This is true especially if the shocks act in an offsetting manner, but
sigma-convergence may fail even if the shocks are purely idiosyncratic (i.e., uncorrelated with incomes per capita). Young, Higgins, and Levy (2008) discuss the difference between beta- and sigma-convergence and document the absence ofsigma-convergence for U.S. counties.

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I note that my data come from the United Nations Industrial
Development Organization’s (UNIDO) industrial statistics database, which is derived largely from industrial surveys. Because
microenterprises and informal firms are often excluded from such
surveys, we cannot be certain that the results are universally
valid across all types of manufacturing activities. In the absenceof more complete coverage of manufacturing, these findings onconvergence should be as viewed as applying to the organized,
formal parts of manufacturing.
To my knowledge, this is the first article to demonstrate unconditional convergence in industry for a wide range of countries
and for detailed manufacturing industries. There does not seemto be any work that has looked at highly disaggregated data for
manufacturing or at the manufacturing experience of countries
beyond Organisation for Economic Co-operation and Development (OECD) and U.S. states (Bernard and Jones 1996a.
1996b; see also Sørensen 2001). However, two recent related studies deserve mention. In unpublished work, Hwang (2007) has
documented that there is a tendency for unconditional convergence in export unit values in highly disaggregated productlines. Once a country begins to export something, it travels up
the value chain in that product regardless of domestic policies or
institutions.5 Hwang shows that the lower the average unit
values of a country’s manufactured exports, the faster the country’s subsequent growth, unconditionally. This article differsfrom Hwang in that it focuses on output rather than exports
and directly on productivity (rather than unit values). Convergence seems to kick in manufacturing regardless of whether production is exported.6 In addition, a recent paper by Bénétrix,
O’Rourke, and Williamson (2012) has documented convergencein industrial output since the late nineteenth century, with thestrongest results obtaining for the 1890–1972 period. Bénétrix
and colleagues focus on growth rates of aggregate industrial

5. Hwang demonstrates his result for both 10-digit U.S. HS import statistics
and 4-digit SITC world trade statistics. The first classification contains thousandsof separate product lines.
6. Also related is a paper by Levchenko and Zhang (2011), which estimatesmodel-based relative productivity trends for 19 manufacturing industries from the
1960s through the 2000 and show that there has been steady convergence acrosscountries.

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171

output rather than on disaggregated industrial productivity,
which is the focus here.7
These results naturally raise the question of why convergence within manufacturing fails to aggregate up to the economy
as a whole. Another contribution of this article is to reconcile
unconditional convergence in manufacturing with its apparent
absence for GDP per worker. I analyze this question by combining
UNIDO data with Penn World Tables to derive implied laborproductivity estimates for the rest of the economy. With these
data, I document three additional features of the data. First, nonmanufacturing does not exhibit convergence. Second, manufacturing’s impact on aggregate convergence is curtailed by its small
size, especially in the poorer countries. Third, the growth boost
from reallocation—the shift of labor to more productive manufacturing—is not sufficiently and systematically greater in poorer
economies. Taken together, these three facts account for the absence of aggregate convergence.
The analysis highlights the role of structural factors, in particular the slow (and sometimes perverse) movement of resources
across economic activities with different convergence characteristics. The trouble from a convergence standpoint is that
economic activities that are good at absorbing advanced technologies are not necessarily good at absorbing labor. As a result, toomuch of an economy’s resources can get stuck in the ‘‘wrong’’
sectors—those that are not on the escalator up. When firmsthat are part of international production networks or otherwise
benefit from globalization employ little labor, the gains remain
limited. Even worse, intersectoral labor flows can be perverse
with the consequence that convergence within the ‘‘advanced’’
sectors is accompanied by divergence on the part of the economy
as a whole. McMillan and Rodrik (2011) illustrate some of theseperverse outcomes using the experience of specific countries.
The article proceeds as follows. Section II describes the data
and methods used for the estimation. Section III presents thebasic results and various robustness checks. Section IV considers
the reasons for convergence failing to aggregate up to the level ofthe entire economy. Section V provides concluding remarks.

7. The first version of this article (Rodrik 2011a) was completed before I
became aware of Bénétrix, O’Rourke, and Williamson’s (2012) work on the subject.

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II. Data and Methods
II.A. Date Source and Description
I use data from UNIDO’s INDSTAT2 and INDSTAT4 databases, which provide industrial statistics for a wide range of countries at different levels of disaggregation (UNIDO 2011, 2012).
My baseline sample is based on INDSTAT2, which has goodcoverage of countries at the ISIC two-digit level going back to
the 1960s. These statistics cover value added and employment,
among others, for up to 23 manufacturing industries per country,
allowing me to calculate labor productivity (value added per employee) and its growth at that level of disaggregation. INDSTAT4
provides more disaggregated data at the four-digit level for up to
127 industries, but covers fewer countries and is spotty for earlier
years, making it impractical to work with it for periods that
extend before 1990. An earlier version of this article reported
substantially similar results using INDSTAT4 (Rodrik 2011a).
Here, I rely mostly on INDSTAT2, which has the advantage ofallowing me to increase the country coverage as well as present
results for before 1990.8 Results using INDSTAT4 serve to provide robustness checks.
Even with INDSTAT2 I am hampered by the fact that few
countries have long time series, with developing countries posing
a particular challenge. The longer the time span we choose for theconvergence horizon, the smaller the number of countries thatcan be included. This makes any horizon longer than a decade
impractical. Accordingly, the empirical analysis is based on decadal growth. Moreover, there is limited overlap across countries
for any particular decade, so that choosing a fixed time span—say, 1995 to 2005—reduces the sample considerably, by half or
more.
In light of these limitations, my baseline consists of a pooled
sample that combines the latest 10-year period for each country
with data. The advantage of pooling is that it maximizes the number of countries that can be included, yielding a sample of 118
countries in all and allowing good coverage of poorer economies.

8. INDSTAT2 was released after Rodrik (2011a) was completed. I thank areferee for bringing it to my attention.

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173

Because each country enters with around 20 industries, the total
number of observations in the baseline specification is greaterthan 2,000. I supplement this sample with two others to checkfor robustness and carry out further analyses:(1) A panel, which stacks the four decades 1965–75,
1975–85, 1985–95, and 1995–2005 for all countries
with data for at least one of those decades. The panel
sample covers a relatively large number of countries(99 in all). Its disadvantage is that it is highly unbalanced, with developed countries having much better
coverage than developing ones. For example, Japan
enters the panel with 71 industries, whereas Algeria
has merely 12.
(2) A pure cross-section for 1995–2005. This sample has
the fewest countries covered (58), for reasons alreadyexplained. I rely on the cross-section sample when I
require a consistent comparison across countries
over a fixed time period (as when I analyze sigmaconvergence or examine the failure of aggregation).
Some countries in INDSTAT2 have data for aggregate manufacturing, but lack disaggregated data. Thus the samples
become slightly larger than what was just noted when I analyze
manufacturing as a whole.
As mentioned in Section I, UNIDO’s data come from industrial surveys whose coverage differs across countries. Data fordeveloped countries refer for the most part to ‘‘all establishments.’’ But in developing countries, enterprises with fewer
than 5 or 10 employees are often not included. For this reason,
the convergence results that follow should be read as applying to
the more formal, organized parts of manufacturing and not tomicro-enterprises or informal firms. An appendix (available on
request) provides a summary of data coverage for each country
included in the regressions.
An important problem with the data is that INDSTAT provides figures for value added in nominal U.S. dollars. What I needis a measure of growth in labor productivity in real terms. I canrecover the convergence parameter under certain assumptions
about the process followed by U.S. dollar inflation rate for disaggregated manufacturing industries. In what follows, I explain myapproach in greater detail.

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II.B. Empirical Specification
Dividing nominal US$ value added by employment, I calculate nominal labor productivity for each industry vijt , where i denotes the industry, j the country, and t the time period. The rate of
growth of labor productivity in real terms, y^ijt , is given in turn byy^ijt ¼ v^ ijt  ijt , where ijt is the increase in the industry-level deflator in dollar terms and a hat over a variable denotes percent
changes.
I assume (real) labor productivity growth in each industry isa function of both country-specific conditions and a convergenceeffect. The latter, in turn, is proportional to the gap between eachindustry’s initial productivity and its frontier technology, represented by vit . Hence:y^ijt ¼ ð ln vit  ln vijt ÞþDj ,where Dj is a dummy variable that stands in for all time- andindustry-invariant country-specific factors. The convergence coefficient we are interested in estimating is . Note that if ln vijt ismeasured with error, this specification potentially introduces abias toward overestimating the rate of convergence, since such anerror weakens the link between initial productivity and final
productivity. This is a common problem in the empirical literature on convergence (Temple 1998).
The last step is to specify a process for prices. I assume acommon global (U.S. dollar) inflation rate for each individual industry up to an idiosyncratic (random) error term, such that
ijt ¼ it þ "ijt . This is a reasonable assumption because manufactures are tradable and face common world prices. Of course, inpractice there are many reasons domestic prices may diverge
from world prices, even in tradables. Transport costs or trade
policies such as import tariffs and export subsidies drive
wedges between domestic and foreign prices. But these wedges
introduce differences in price levels, not inflation rates.
Equivalently, I assume that dollar inflation rates are not systematically correlated with an industry’s distance from the technological frontier. My results do not overstate convergence as longas dollar price inflation is not systematically higher in industries
that are furthest away from the technological frontier. I postpone
further discussion of these issues to the next section, where I
discuss potential complications arising from the absence of
price information more extensively.

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175

This allows me to express the growth of nominal labor productivity as follows:ð1Þ

v^ ijt ¼ ðln vijt  ln vit Þ þ it þ Dj þ "ijt :I assume "ijt is uncorrelated with other explanatory variables
and captures all other idiosyncratic influences on measured laborproductivity. Rearranging terms, I now have the final estimating
equation:
ð2Þ

v^ ijt ¼  ln vijt þ ðit þ  ln vit Þ þ Dj þ "ijt :This can be expressed equivalently as

ð3Þv^ ijt ¼  ln vijt þ Dit þ Dj þ "ijt ,where Dit stands for ðit þ  ln vit Þ. Hence, I can regress thegrowth of labor productivity in nominal U.S. dollar terms on
the initial level of labor productivity, a set of industry  time
period fixed effects (Dit ), and country fixed effects (Dj ). In the
regressions that follow, I include separate industry and period
dummies to soak up any additional confounding residual systematic variation.
It is also possible to run this regression over a single time
period as a pure cross-section. In this case, the industry  time
period fixed effects are reduced to industry fixed effects:
ð30 Þv^ ij ¼  ln vij þ Di þ Dj þ "ij :As specified, the estimate of  will be a measure of conditional convergence, since country-specific conditions are explicitly controlled for by the country fixed effects. A test of
unconditional convergence consists of dropping these country
dummies and checking whether the estimated coefficient – remains negative and statistically significant.

III. Empirical Results
III.A. Basic Results
Table I shows the results for the baseline specification alongwith its variants. The dependent variable in each case is the (compound annual) growth rate of labor productivity for two-digit
manufacturing industries. The regressors are the log of initialTABLE I

(1)

(2)

Yes
Yes
Yes

Yes
118

2,122

Yes
118

2,122

0.054***(0.006)

No
Yes
Yes

0.029***(0.004)

1,861

Yes
104

No
Yes
Yes

0.029***(0.004)

(3)

1,861

Yes
104

Yes
Yes
Yes

0.057***(0.006)(4)

Baseline, only post-1990 data

955

Yes
58

No
Yes
Yes

0.024***(0.004)

(5)

955

Yes
58

Yes
Yes
Yes

0.059***(0.010)(6)

Cross-section, 1995–2005
(7)

3,848

Yes
99

No
Yes
Yes

0.018***(0.003)

(8)

3,848

Yes
99

Yes
Yes
Yes

0.060***(0.005)

(9)

3,848

Yes
99

0.019***(0.006)0.000(0.006)0.016*(0.009)
0.006(0.007)
No
Yes
Yes

Panel
(10)

3,848

Yes
99

0.055***(0.007)
0.009(0.006)0.008(0.007)
0.011(0.009)
Yes
Yes
Yes

Notes. The baseline specification uses all countries with the requisite data, using only the latest 10-year period for each. The panel specification pools data for 1965–75,
1975–85, 1985–95, and 1995–2005 for all countries with requisite data. Standard errors are clustered at country level.
***p < .01, **p < .05, *p < .10.

Log initial productivity,
1975–85
Log initial productivity,
1985–95
Log initial productivity,
1995–2005
Country fixed effects
Industry fixed effects
Period fixed effects
Period  industry
fixed effects
Number of countries
Number ofobservations

Log initial productivity

Baseline

BASELINE SPECIFICATION AND VARIATIONS (DEPENDENT VARIABLE: GROWTH OF LABOR PRODUCTIVITY IN TWO-DIGIT MANUFACTURING INDUSTRIES OVERA DECADE)176
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177

labor productivity and a host of fixed effects, depending on thespecification. Each regression is run first without and then withcountry dummies. As explained previously, these two specifications yield the unconditional and conditional convergence coefficients, respectively. Standard errors are clustered at the country
level in all specifications.
Column (1) is the baseline result, corresponding to the scatter plot displayed in Figure II. The estimated convergence coefficient of 2.9% is highly significant, with a t-statistic of 6.95. Recall
that the baseline sample pools different decades for each country.
There is little evidence of parameter heterogeneity across different time periods, however. I get quite similar estimates when I
drop pre-1990 observations from the baseline specification(column (3)) or when I run a pure cross-section for the more
recent period 1995–2005 (column (5)).9 I take note of the factthat the 1995–2005 results do not diverge much from the baseline
as I have to focus on the cross-section for some of the subsequent
analysis.
The panel specification, which combines the four decades
1965–75, 1975–85, 1985–95, and 1995–2005, yields a somewhat
smaller  of 1.8%, but this estimate is still highly significant witha t-statistic of 6.74 (column (7)). In column (9) I test explicitly for
differences in the convergence coefficient across time periods byinteracting base labor productivity with decade dummies. The
excluded decade is 1965–75. The results suggest a lower convergence estimate for 1985–95 but otherwise no strongly discernible
trends over time. In particular, there is no evidence of stronger
convergence in more recent decades. This is somewhat surprisingbecause one might have expected globalization and the spread ofglobal production networks to greatly facilitate technologicaldissemination and therefore catch-up. The result suggests convergence is an intrinsic property of manufacturing industries,
and one that is not driven by the ups and downs of global economic integration.
Each specification in Table I is paired with its conditionalvariant, which includes country fixed effects. The estimated convergence coefficients always increase in size, typically doubling—or tripling, in the panel specification—when country dummies
9. The cross-section results for the latest decade for which I have data,
1997–2007, are very similar to those for 1995–2005 (not shown). I present the
1995–2005 results because of the somewhat larger country coverage.

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are included. This is in line with the conditional convergence results in the literature. As noted recently by Barro (2012), there
are reasons to think growth regressions with country fixed effects
yield upwardly biased estimates of the convergence rate when thetime horizon is short. This is due to the so-called Hurwicz-Nickell
bias: the estimate of the coefficient on the lagged dependent variable is biased downwards in the presence of a fixed effect(Hurwicz 1950; Nickell 1981). The bias tends to zero as the timespan gets large, but can be large in short panels.10 For my purposes, because I am mainly interested in unconditional convergence (and hence the specifications without country fixed effects),
it suffices to treat the conditional convergence estimates as upperbounds. They still provide a useful reference point for gauging themagnitude of the unconditional convergence rates.
It is to be expected that country-specific conditions that are
correlated with initial productivity—policies, institutions, geography—play a role in determining the speed of catch-up. So evenin the absence of the Hurwicz-Nickell bias, it is natural that thebeta coefficient would become larger when fixed effects are introduced. What is surprising is the evidence for systematic and rapid
productivity convergence in individual manufacturing industries
when these country-specific conditions are not controlled forthrough country dummies. Once again, there is no evidence ofsignificant differences in convergence rates across time periods.
In particular, the speed of (conditional) convergence does notappear to have increased in recent decades (column (10)).
In Table II, I compare the baseline with results at different
levels of aggregation. The table shows specifications that are bothmore disaggregated (three- and four-digit, using INDSTAT4
data) and more aggregated (manufacturing as a whole). The results in column (7) correspond to Figure III. Each regression is
run in two versions, one with the largest possible sample andanother one that restricts the sample to a common list of countries with the requisite data at all levels of aggregation. The latter
facilitates direct comparison across different levels of aggregation. The estimated s are statistically significant, typically at
10. In principle, the same bias exists when industry fixed effects are included
too, as in my baseline specification. But the dimensionality is much greater in thiscase since the de-meaning of the dependent variable takes place over both time
periods and across countries. In any case, I also provide pure cross-section estimates for each industry separately, using no fixed effects.

TABLE II0.028***(0.008)
No
Yes
Yes
Yes
38
812

0.029***(0.004)
No
Yes
Yes

Yes
118

2,122

(2)
Commonsample

704

Yes
39

0.029***(0.006)
No
Yes
Yes

All

(3)

703

Yes
38

0.030***(0.006)
No
Yes
Yes

(4)
CommonsampleISIC 3-digit

2,871

Yes
39

0.031***(0.004)
No
Yes
Yes

All

(5)

2,869

Yes
38

0.031***(0.004)
No
Yes
Yes

(6)
CommonsampleISIC 4-digit

123

No
123

0.030***(0.006)
No
No
Yes

All

(7)

—

38

38

0.023**(0.009)
—
—
Yes

(8)
CommonsampleAll manufacturing

Notes. Standard errors are clustered at country level, except for ‘‘all manufacturing,’’ for which robust standard errors are reported. Common sample refers to the set ofcountries that have data at all levels of aggregation showed in this table, for the same period.
***p < .01, **p < .05, *p < .10.

Country fixed effects
Industry fixed effects
Period fixed effects
Period  industry
fixed effects
Number of countries
Number ofobservations

Log initial productivity

All

(1)

Baseline (ISIC 2-digit)

COMPARISON OF RESULTS AT DIFFERENT LEVELS OF AGGREGATION (DEPENDENT VARIABLE: GROWTH OF LABOR PRODUCTIVITY IN MANUFACTURING
INDUSTRIES OVER A DECADE)UNCONDITIONAL CONVERGENCE IN MANUFACTURING

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the 99% confidence level, at all levels of aggregation. The estimates at the three- and four-digit level are remarkably similar tothe baseline estimates, as are the estimates for manufacturing as
a whole. Looking across the regressions with a common sample,
one can see some indication that the estimated coefficient increases with the level of disaggregation—from 2.3% for manufacturing as a whole to 3.1% at the four-digit level. But thesedifferences are not statistically significant. The similarity in themagnitude of the estimates suggests that the failure of unconditional convergence to aggregate up to the economy as a whole is
not a result of compositional issues within manufacturing. I makeuse of this result later in the article.
III.B. Robustness
Table III presents a battery of additional robustness tests
around the baseline specification. Panel A prunes the baseline
sample in a number of ways to ensure that countries/industries
with specific characteristics are not driving the results. I exclude
in turn (1) countries with fewer than 10 industries; (2) observations that correspond to the highest and lowest 10% values for
growth; (3) observations in the top and bottom half, respectively,of the sample in terms of labor productivity; (4) former socialist
countries; and (5) OECD countries.
Remarkably, the convergence estimates remains highly significant across all these runs. They vary from a low of 1.1% whengrowth observations at the top and bottom deciles are excluded(column (3)) to a high of 6.0% for the low-productivity half of thesample (column (4)). Note that the result becomes, if anything,stronger when OECD countries are excluded (column (7)), withthe convergence coefficient rising from 2.9% to 3.8%. This is significant, because unconditional convergence has never been documented outside the OECD.
Panel B of Table III carries out different types of robustness
tests, including weighting observations by value added, instrumenting initial labor productivity by lagged productivity (a checkagainst measurement error), recalculating growth rates by estimating a log-linear trend using all 10 annual observations(instead of just endpoints),11 and clustering standard errors by
11. This guards against introducing a spurious bias that may arise from laggedlabor productivity appearing directly on both sides of the regression equation, with
opposite signs.

Country fixed effects
Industry fixed effects
Period fixed effects
Period  industry fixed effects
Number of countries
Number of observations

Log initial productivity

TABLE III

0.029***(0.004)
no
yes
yes
yes
118
2,122

Baseline(1)0.027***(0.004)
no
yes
yes
yes
109
2,058

(2)
Excludingcountries
with fewer
than 10industries
included0.011***(0.003)
no
yes
yes
yes
116
1,697

Excludingobservations
with the highest
and lowest 10%
values for growth(3)

Panel A

0.060***(0.006)
no
yes
yes
yes
95
1,061

(4)
Subsample
with laborproductivity
in the bottom
half of thesample0.025***(0.004)
no
yes
yes
yes
112
1,997

Excludingformersocialist
countries

Subsample
with laborproductivity
in the top halfof the sample
0.043***(0.005)
no
yes
yes
yes
108
1,061

(6)

(5)

ROBUSTNESS TESTS (DEPENDENT VARIABLE: GROWTH RATE OF PRODUCTIVITY OVER RELEVANT PERIOD)

0.038***(0.006)
no
yes
yes
yes
91
1,542

ExcludingOECDcountries(7)

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

181

no
yes
yes
yes
118
2,122

0.030***(0.004)

no
yes
yes
yes
104
1,615

no
yes
yes
yes
102
1,754

0.030***(0.004)

Rescaled laborproductivity
growth, adjusted
for real exchange
rate appreciation

IV resultswith 5-year
lagged initial
productivity as
instrument
0.024***(0.005)(10)

Panel B
(9)

Notes. Standard errors are clustered at country level, unless otherwise noted.
***p < .01, **p < .05, *p < .10.

Log initial productivity,
lowest quartile
Log initial productivity,
second quartile
Log initial productivity,
third quartile
Log initial productivity,
fourth quartile
Country fixed effects
Industry fixed effects
Period fixed effects
Period  industry fixed effects
Number of countries
Number of observations

Log initial productivity(8)
Weightedregression
results:ln (value
added)
weights

TABLE III
(CONTINUED)

no
yes
yes
yes
23
2,122

0.029***(0.003)Standard
errorsclusteredby industry

(11)

yes
no
yes
no
118
2,122

0.021***(0.003)(12)
Country
fixedeffects
with noindustry
controls

no
yes
yes
yes
107
1,774

0.017***(0.006)(13)
Growth rates
calculated
from log-linear
trend using
10 annual
observations

0.034***(0.007)
0.035***(0.006)
0.034***(0.006)
0.032***(0.005)
no
yes
yes
yes
118
2,122

Testing for
nonlinearity
by labor
productivity
quartiles

(14)

182
QUARTERLY JOURNAL OF ECONOMICSUNCONDITIONAL CONVERGENCE IN MANUFACTURING

183

industry rather than country. In all these runs, the results
remain highly significant. Note in particular that the estimatedcoefficient remains virtually unchanged when I weight industries
by size (column (8)).12 Hence, the convergence result is not driven
by the experience of relatively small industries. Instrumenting
for initial productivity reduces the estimated  marginally to
2.4% (column (9)).
The last column of Table III tests directly for nonlinearity of 
by allowing the estimated coefficient to vary by labor productivity
quartile. The results do not suggest any nonlinearity—at least at
the two-digit level (column (14)). There is greater evidence of
nonlinearity in the four-digit data (INDSTAT4), as I reported in
the earlier version of this article (Rodrik 2011a).13
Another way to examine the data is to scrutinize the convergence evidence on an industry-by-industry basis. This exercise
not only serves as a robustness test but is interesting in its own
right. I begin with a few scatter plots for individual industries.
Figure IV shows convergence plots for the four largest industries
in the sample that also have good country coverage: ISIC 15 (foodand beverages), 24 (chemicals and chemical products), 29 (machinery and equipment, n.e.c.), and 34 (motor vehicles). (In keeping with the baseline specification, each country enters thesescatterplots with the latest period for which it has data. I continue to control for time-specific inflation trends by including
period dummies.) Note that these plots use data from just the
specified industries. As the negative slopes indicate, countries
that started further behind tended to experience more rapid productivity growth in all four industries. The relationship is statistically significant at the 99% level in all four cases, withestimated coefficients ranging from 2.2% (ISIC 34) to 2.8%
(ISIC 15).
The individual convergence coefficients estimated on anindustry-by-industry basis for each of our two-digit industries
are shown in Table IV. I regress, separately for each industry,
the growth rate of an industry’s labor productivity against its

12. Using employment weights produces nearly identical results.
13. In Rodrik (2011a) I also reported some evidence that industries differ intheir  coefficients. These differences are much less evident in the two-digit data
I use here. The only finding of note is that textiles and clothing show a mildly lower
convergence coefficient. These results, not shown here, are available on request.

184

QUARTERLY JOURNAL OF ECONOMICSFIGURE IV
Convergence in Specific Industries
Vertical axis represents relative growth rate of labor productivity, controlling for period fixed effects. Each country enters with most recent decade forwhich data are available.

initial level across all countries in the sample that have therequisite data for the 1995–2005 period:v^ ij ¼ i ln vij þ "ij

fori ¼ 1; ::: ; I:This entails running as many regressions (23) as I have manufacturing industries. This is the direct analogue of runningcross-country growth regressions. Note that these specifications
do not contain industry, period, or any other fixed effects. Many ofthese regressions cover 30–40 countries, so one should not be too
demanding in terms of statistical significance for industryspecific estimates. Nevertheless, the results are quite strong.
Among the 23 industries, I find that 18 have statistically significant convergence coefficients, ranging from a low of 1.4% (textiles) to a high of 5.5% (office, accounting, and computing
machinery). All but one of the industries exhibit unconditional
convergence, with wearing apparel the sole exception.

TABLE IV

Total manufacturing
Food and beverages
Tobacco products
TextilesWearing apparel, fur
Leather, leather products, and footwear
Wood products (excl. furniture)
Paper and paper products
Printing and publishing
Coke, refined petroleum products, nuclear fuelChemicals and chemical products
Rubber and plastics products
Nonmetallic mineral products
Basic metals
Fabricated metal products
Machinery and equipment n.e.c.
Office, accounting, and computing machinery
Electrical machinery and apparatus
Radio, television, and communication equipment
Medical, precision, and optical instruments
Motor vehicles, trailers, semi-trailers
Other transport equipment
Furniture; manufacturing n.e.c.
Recycling

D
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

63
43
31
54
34
34
53
52
53
38
52
53
56
50
53
37
23
32
26
45
35
27
52
22

Number of countries

Notes. These coefficients are obtained by running pure cross-section regressions for each industry separately.
***p < .01, **p < .05, *p < .10, n.s. = not significant.

Sector

ISIC code

BETA-CONVERGENCE COEFFICIENTS BY INDUSTRY, 1995–2005 REGRESSIONS

.020
.026
.020
.014
.005
.001
.033
.024
.021
.026
.010
.019
.032
.046
.022
.011
.055
.023
.043
.031
.019
.030
.031
.019

Beta coefficient

***
***
**
*
n.s.
n.s.
***
***
***
**
n.s.
***
***
***
**
n.s.
***
***
***
***
*
**
**
n.s.

Significance

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

185

186

QUARTERLY JOURNAL OF ECONOMICS

For an even more disaggregated analysis, I turn to
INDSTAT4, which breaks down manufacturing into 127 separate
subsectors. This allows a more fine-tuned scrutiny of the convergence experience of individual industries, albeit for a reduced
country sample. As I have shown in Rodrik (2011a, table 2),
four-digit data yield highly significant global convergence coefficients when all four-digit industries are pooled together, even for
periods that are as short as five years. Here I focus on the resultsof individual industry regressions for 2000–2005, because thismaximizes the number of countries (and hence observations).
Once again, these are simple cross-section regressions for eachindustry separately, with no fixed effects.
Figure V summarizes the results by showing the distributionof estimated coefficients on initial productivity across the 127
industries, normalized by their standard errors. The vast majority of the estimates are negative. Though not all of them are statistically significant, a surprising number of the negative ones are.
Specifically, 79 (out of 127) of the industry regressions yield negative and statistically significant coefficients (at the 95% level orhigher). By contrast, none of the (few) positive coefficients are
statistically significant.
There is a statistical sense in which the global estimate,which pools across industries and controls for industry fixed
effects, represents a weighted average of these industry-byindustry convergence estimates. Specifically, consider a given
cross-section (such as 2000–2005 in the foregoing). Omitting
time subscripts, the relationship between estimates  and i isgiven by:!

var ln vij jJ ¼ i PrðJ ¼ iÞ
 i PI¼,
i¼1
l¼1 var ln vij jJ ¼ l PrðJ ¼ lÞ
IX

where J denotes an industry-identifying variable. Thus, theweight that each industry estimate gets in the global estimate
depends on the variance of the independent variable ln vij
within the industry as well as the relative number of occurrencesof that industry in the global sample.14

14. The derivation of this expression, for which I am indebted to Alberto
Abadie, is available on request.

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

187

FIGURE V
Distribution of Convergence Coefficients, by 4-digit Industry (Normalized byStandard Error)
The figure is based on convergence coefficients generated from 127 industry
convergence regressions across 4-digit manufacturing industries. See text.

III.C. Complications Arising from Price Effects
One possible concern in interpreting these results is that myassumption of a common value added deflator (in dollars) for eachindustry, regardless of the country where it is located, may beintroducing a bias to the estimation. I justified this assumption
previously by arguing that the manufacturing industries in question are tradable, and hence face common world prices. Of course,
I do not expect the law of one price to obtain perfectly, even forhomogeneous or standardized goods. Tariffs, subsidies, nontariffbarriers, and transport costs often drive a wedge between domestic
and world prices. Such wedges may well be larger in the poorer
countries. My estimation strategy can accommodate such deviations provided they do not vary over time in a way that is correlated systematically with distance from the technological frontier.
Suppose, for example, the ad valorem equivalent of trade
costs is ijt . Then ijt ¼ it þ ð1 þ ijt Þ1 dijt , where it is the

188

QUARTERLY JOURNAL OF ECONOMICS

percent change in world prices. All I need for unbiased estimation
is for ð1 þ ijt Þ1 dijt to be uncorrelated with initial labor productivity ln vijt , conditional on a set of industry, period, and industry  period fixed effects. It is hard to think of strong reasons apriori as to why there should be such a pattern in trade costchanges.
One potential source of bias is systematic changes in realexchange rates. In principle, across-the-board increases in domestic costs such as wages should be offset, on average, by depreciation of the home currency, leaving dollar values generally
unchanged. But in countries that experience sustained movements in their real exchange rate, trends in value added expressed in U.S. dollars will be misleading with regard to
productivity in individual manufacturing industries. The worst
case, from the perspective of the present article, would be if the
low-income countries that house a preponderant share of
low-productivity industries were the ones to experience real exchange rate appreciations—a rise in domestic costs not compensated by currency depreciation. This would lead to an upwardbias in my convergence estimates.
To check against this possibility, Table III provides a versionof the convergence regression that explicitly ‘‘corrects’’ for realexchange rate changes. I rescaled the growth in value added
per worker by deflating it with (one plus) the rate of appreciationof the country’s real exchange rate.15 This reduces—across theboard—the measured productivity growth rates of industries incountries which have experienced real appreciations, while raising them in countries with real depreciations. I rerun the baseline
specification using these adjusted values for the dependent variable (Table III, column (10)).
If observed convergence were due to real appreciation in thepoorer countries, the resulting estimates would be substantiallylower. In fact, the estimated  is actually slightly higher (.030)
and statistically equally significant. (Note, however, that the
sample size is somewhat reduced as the lack of price data forsome of the countries prevents me from computing real exchange
15. These are conventional bilateral real exchange rates vis-à-vis the UnitedStates. Domestic inflation rates have been calculated using producer-price indiceswhere possible, substituting the Consumer Price Index where the Producer Price
Index is not available. The source for the data on exchange rates and price indices is
the International Monetary Fund’s International Financial Statistics. A few countries had to be dropped because of lack of price data.

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

189

rate changes for them.) The bottom line is that there is no evidence that real exchange rate movements have distorted my basic
findings.
Another source of possible bias arises from compositionalchanges. Even four-digit industries are a mix of different activities, and what appears as an increase in dollar values may be inreality a shift toward higher value-added activities within thesame industry. Equivalently, producers may be moving towardhigher-quality varieties, so that what looks like productivity
growth is really quality upgrading (Schott 2004; Hwang 2007).
It is possible that industries that start further away from thefrontier experience such shifts more rapidly. In the presence of
price data, I would have been able to capture such changes
through their effect on industry-specific prices.
Such biases are potentially of concern, but my results provide
some comfort that they are not very important in practice. As
seen in Table II, the estimated speed of convergence changes
very little across two-, three-, and four-digit levels of aggregation.
If anything, the estimate increases in value as I disaggregate
further. If the observed productivity growth was due to a shift
toward higher quality products, the opposite result would hold.
Furthermore, even if the compositional biases were quantitatively significant, they would not detract greatly from the convergence result that is my focus. Moving into more sophisticated,higher value-added products and increasing physical productivity are both ways of raising the available returns to labor. They
are both channels for income convergence. To the extent that
quality upgrading takes place generally, and it does so more rapidly in the poorer countries, it is simply another manifestation of
the productive convergence I am interested in documenting.
A final interpretational difficulty relates to the possible roleof entry barriers in accounting for my results. The absence of
prices in the data forces me to conflate ‘‘revenue productivities’’
with ‘‘quantity productivity’’ (Foster, Haltiwanger, and Syverson
2008; Hsieh and Klenow 2009). Dollar values of productivity—‘‘revenue productivities’’—can change as a result of movements inprices as well as in physical productivities. The former, in turn,can be driven by shifts in entry barriers, independently of any
changes in physical productivity.
Consider, for example, an economy in which the failure of
marginal value labor productivities to equalize across sectors isdue to barriers to labor mobility. As in Hsieh and Klenow (2009),

190

QUARTERLY JOURNAL OF ECONOMICS

I could suppose these barriers are government-imposed restrictions, such as preferential licensing or credit policies that advantage some firms or sectors over others. In this world, differencesin dollar labor productivities within countries would reflect themagnitude of these barriers, and my regressions would be capturing convergence and divergence in them.16 In particular, the ‘‘unconditional convergence’’ result may reflect the fact thatindustries with high barriers have experienced a decline in barriers while industries with low barriers have experienced an increase. Interesting as this reading of the evidence may be in its
own right, it would be quite a different result than one about
productivity convergence.
However, in this case I am looking at industries across aswell as within countries. In fact, as the industry-by-industry regressions discussed previously (and the sigma-convergence
results, discussed later) indicate, an essential part of the identification for unconditional convergence comes from the variation
within industries across different countries. Revenue productivities are converging globally. It is not very plausible to attributedifferences in dollar labor productivities in, say, motor vehicles
across countries to differences in entry barriers faced by motor
vehicle producers in different countries, or to attribute convergence in revenue productivities to systematic changes in suchbarriers.
In addition, because low-productivity industries are in poor
countries, for changes in labor mobility barriers to account for myfindings, mobility barriers must have come down in poorer countries and increased in the rich countries. If this were true, I would
also see much more rapid expansion in manufacturing in poor
countries. As I show later when I discuss aggregation issues,
this doesn’t seem to be the case. The movement of labor into the
manufacturing industries of the poorer countries is not significant or rapid enough to support such a conjecture. The sluggish
pace of labor movement, in particular the relatively slow expansion of manufacturing in the poorest countries, does not suggest
systematic changes in barriers to mobility.

16. I am grateful to a referee for raising this possibility and for the related
discussion on revenue versus quantity productivity.

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

191

III.D. Sigma-Convergence
Beta-convergence does not guarantee sigma-convergence ina world where growth rates are driven not just by the forces of
convergence but also by other determinants and shocks. The present data do not allow a very comprehensive analysis of sigmaconvergence because I need to have a large sample of countries
with data both at the beginning and end of the period to determine whether the dispersion of productivity has diminished overany given time horizon. As discussed previously, the country
coverage shrinks dramatically when I restrict the sample to across-section of any fixed time period.
The best I can do is to choose a time period that maximizes
the number of countries that can be included. Table V shows theresults for the 1995–2005 decade, which covers a variable numberof countries across different industries, and 63 countries for manufacturing as a whole. The overall pattern across two-digit manufacturing industries seems mixed, but a majority (16) shows
declines in dispersion, some quite dramatically. Basic metals(ISIC 27); office, accounting, and computing machinery (ISIC30); and radio, television, and communication equipment (ISIC32) have all experienced substantial reductions in the standard
deviation of log productivity, of the order of 30 log-points or more,in a period as short as a decade. More remarkably, dispersion in
manufacturing as a whole has come down by 10 log-points in mysample of 63 countries. This amounts to a reduction of 10% in
dispersion.
Figures VI and VII provide a visual sense of these findings.
The distribution of aggregate manufacturing productivity has aclear twin-peaked shape. Between 1995 and 2005, the two peaks
have moved considerably closer to each other (Figure VI).

IV. Why Unconditional Convergence Does Not
Aggregate Up
The forces of convergence seem quite strong in manufacturing industries. It stands to reason that one ould uncover similar
results for certain other parts of the economy as well, perhaps
modern, tradable services such as financial or business services,among others. One might expect convergence at the sectoral levelto produce aggregate convergence as well, unless there are countervailing forces pushing in the other direction. Yet the aggregateTABLE V

SectorTotal manufacturing
Food and beverages
Tobacco products
TextilesWearing apparel, fur
Leather, leather products, and footwear
Wood products (excl. furniture)
Paper and paper products
Printing and publishing
Coke, refined petroleum products, nuclear fuelChemicals and chemical products
Rubber and plastics products
Nonmetallic mineral products
Basic metals
Fabricated metal products
Machinery and equipment n.e.c.
Office, accounting, and computing machinery
Electrical machinery and apparatus
Radio, television, and communication equipment
Medical, precision, and optical instruments
Motor vehicles, trailers, semi-trailers
Other transport equipment
Furniture; manufacturing n.e.c.
Recycling

ISIC code

D
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

63
43
31
54
34
34
53
52
53
38
52
53
56
50
53
37
23
32
26
45
35
27
52
22

Number ofcountries

SIGMA-CONVERGENCE, 1995–2005

1.186
1.086
1.591
1.218
1.166
1.144
1.479
1.188
1.167
1.415
1.286
1.099
1.206
1.291
1.231
1.237
0.994
1.124
1.229
1.380
0.945
1.092
1.257
0.991

Sigma
in 1995
1.082
0.942
1.668
1.246
1.301
1.269
1.154
0.999
1.060
1.835
1.273
0.976
0.992
0.893
1.108
1.247
0.688
0.943
0.914
1.202
1.012
0.944
1.135
0.988

Sigma
in 2005

0.104
0.144
0.077
0.028
0.135
0.125
0.325
0.189
0.1070.420
0.013
0.123
0.214
0.398
0.1230.010
0.306
0.181
0.315
0.1780.067
0.148
0.122
0.003Difference

192
QUARTERLY JOURNAL OF ECONOMICSUNCONDITIONAL CONVERGENCE IN MANUFACTURING

FIGURE VI
Sigma-Convergence, All Manufacturing

FIGURE VII
Sigma-Convergence in Specific Industries

193

194

QUARTERLY JOURNAL OF ECONOMICS

data do not support this conjecture. In this section I consider why
economies as a whole fail to exhibit unconditional converge despite the strong pull of convergence within manufacturingindustries.
Recall (from Table II) that aggregate manufacturing does
exhibit unconditional convergence. There is some evidence thatconvergence gets stronger the more one disaggregates withinmanufacturing. But it is clear that the bulk of the convergence
failure takes place as one goes from manufacturing (in aggregate)
to the rest of the economy. So I focus here on the contrasting
behaviors of manufacturing and the economy (both as a whole),
abstracting from aggregation issues within manufacturing.
To compare manufacturing to the rest of the economy, I needdata that go beyond what I have been using so far. I proceed by
combining INDSTAT2 with Penn World Tables 7.0 (Heston,
Summers, and Aten 2011; PWT), which include data on aggregate
GDP and (implicitly) total employment. Because data for GDP
are in real purchasing power parity (PPP)-adjusted terms in the
PWT, I first convert these to current dollars to render the economywide data directly comparable to INDSTAT2.17 Subtracting
INDSTAT2 manufacturing value added and employment
levels from the aggregates in PWT yields presumptive values
for nonmanufacturing. We thereby obtain labor productivity figures for nonmanufacturing and the entire economy that are consistent with both data sets. The employment shares ofmanufacturing and nonmanufacturing can be imputed in a similar fashion.18
17. PWTs include data on real GDP per worker (rgdpwok), which is
PPP-converted GDP chain per worker at 2005 constant prices. To undertake theconversion, first I recover the conversion factor between current and constantprices, by taking the ratio of current and constant price GDP data in PWT. For
current GDP data, I use PPP-converted GDP per capita, G-K method, at current
prices (in I$) (cgdp). For constant price GDP data, I use PPP-converted GDP per
capita (Laspeyres), derived from growth rates of c, g, i, at 2005 constant prices
cgdp(rgdpl). The ratio of these two values (rgdpl) gives a price conversion factor between
current and constant. Next I calculate a conversion factor between market prices
and PPP. For this I use the PWT values for PPP over GDP in national currency units
per US$ (ppp) divided by the exchange rate to US$ in national currency units per
US$ (xrat). Using these two conversion factors, I convert PWT’s data on real GDP
cgdp
pp
per worker into nominal U.S. dollars (ngdpwok = rgdpwok * (rgdpl) * (xrat)). This gives
nominal GDP chain per worker at current prices (US$).
18. To compute the employment share of manufacturing, I first compute thetotal working population using data from PWTs. To do this, I divide PWT data on

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

195

I can now compare convergence behavior systematically
across different types of activities. I focus in this section on the
1995–2005 cross-section, because these regressions do not mix
different time periods and are the easiest ones to interpret in
the present context. Column (2) of Table VI verifies the absenceof unconditional convergence in nonmanufacturing. The estimated convergence coefficient is very small and statistically indistinguishable from zero. Column (3) replicates the exercise for
economywide labor productivity, again confirming nonconvergence. For comparison purposes, the bottom part of the tableshows the analogous results for the full panel.19
Other columns in Table VI highlight the importance of therelative size of manufacturing in driving convergence behavior.
The employment share of manufacturing (a) appears to be a key
conditioning factor. As columns (5)–(7) show, a is a significant
determinant of economywide growth. More important for my purposes, aggregate convergence seems to be conditional on a.
Comparing columns (3) and (7), one can see that initial economywide productivity turns statistically significant in the aggregate
growth equation once I control for a. These are, of course, sparse
GDP per capita (rgdpch) by data on GDP per worker (rgdpwok). This gives thenumber of workers per capita. From this number and the total population figures(pop) in PWT, I calculate total employment. Total manufacturing employment isgiven by INDSTAT2 as the number of workers in manufacturing as a whole, in ISIC
category D. From this and the total employment number computed using PWT, theemployment share of manufacturing (a) can be calculated. To compute nonmanufacturing labor productivity, I convert PWT data on total PPP converted GDP, G-K
method, at current prices in millions I$ (tcgdp) to nominal GDP, using the PPP–
ppp
exchange rate conversion factor described in the previous note (xrat).
Nonmanufacturing value added is calculated as the difference between this nominal value and the value added for manufacturing as a whole from INDSTAT2, in
ISIC category D. This number is then divided by nonmanufacturing employment(the difference between total employment and total manufacturing employment),
to give nonmanufacturing labor productivity. From these numbers, growth rates
can be calculated to run convergence regressions for nonmanufacturing.
19. Interestingly, nonmanufacturing and the aggregate economy appear to exhibit some unconditional convergence in the baseline sample, at a rate of around
1%, or one third that for manufacturing in the same sample. However, this is notrobust and seems to be a result of the correlation between the initial levels of productivity in manufacturing and nonmanufacturing. When initial manufacturing
productivity is also included in the regression, the coefficient on initial nonmanufacturing productivity actually turns positive in both the aggregate economy andnonmanufacturing growth regressions (while manufacturing productivity is negative and significant).

yes

Yes
95
234

0.010***(0.003)61
61

0.020***(0.006)(1)
Manufacturing

Yes
95
234

0.002
(0.002)yes
61
61

0.002(0.003)(2)
Nonmanufacturing

Yes
95
234

0.003
(0.002)yes
61
61

0.003(0.003)(3)
Economy

yes

Yes
95
234

0.023***(0.005)0.016***(0.004)61
61

0.026***(0.009)0.015**(0.006)(4)
Economyyes

0.309***(0.049)Yes
95
234

0.010*(0.005)-0.002(0.005)61
610.290***(0.093)

0.015*(0.008)0.002(0.007)(5)
EconomyDependent variable: decadal growth of labor productivity in

yes

0.297***(0.034)Yes
95
234

0.011***(0.003)61
61

0.311***(0.071)

0.013***(0.004)(6)
Economyyes0.009***(0.003)0.344***(0.043)
Yes
95
234

61
61

0.010***(0.003)0.386***(0.093)(7)
EconomyNotes. Country sample is limited to those in the 1995–2005 cross-section and panel samples with data for all columns. Panel specification pools data for 1965–75, 1975–85,
1985–95, and 1995–2005. Robust standard errors (Panel A) and standard errors clustered at country level (Panel B) are reported.
***p < .01, **p < .05, *p < .10.

Period fixed effects
Number of countries
Number of observations

Alpha (employment share of manufacturing)Log initial productivity, economy (GDP/worker)

Log initial productivity, nonmanufacturing

Panel B: Panel specification
Log initial productivity, manufacturing

Period fixed effects
Number of countries
Number of observations

Alpha (employment share of manufacturing)Log initial productivity, economy (GDP/worker)

Log initial productivity, nonmanufacturing

Panel A: 1995–2005 cross-section
Log initial productivity, manufacturing

TABLE VI
ECONOMIC STRUCTURE AND AGGREGATE (NON)COVERGENCE196
QUARTERLY JOURNAL OF ECONOMICSUNCONDITIONAL CONVERGENCE IN MANUFACTURING

197

specifications that do not rule out alternative interpretations; acould be proxying for a wide range of other conditioning variables
with which it is correlated. The ‘‘conditional’’ convergence rate
controlling for a is relatively low (1%) in light of estimates inBarro (2012), so this proxying is likely imperfect. In any case,these results are suggestive of the role played by the relative
size of manufacturing in explaining convergence behavior
across countries.
To investigate the issue more formally, I divide the economy
into manufacturing (m) and nonmanufacturing (n) activities.
GDP per worker is the weighted average of labor productivity
in these two activities: y ¼ ym þ ð1  Þyn , where the weight is the share of the economy’s labor force employed in manufacturing. (I dropped country subscripts to avoid clutter.) Growth in
GDP per worker is in turn expressed asy^ ¼ m y^m þ ð1  Þn y^n þ ðm  n Þd,
where a ‘‘^’’ over a variable denotes proportional growth rates as
before, and m ¼ yym and n ¼ yyn are the productivity premia/discounts for the two sectors.
I now impose some further structure on the growth decomposition, using the convergence results obtained thus far. In
particular, I write the growth rates of manufacturing and nonmanufacturing as:y^n ¼ gy^m ¼ g þ  ln y  ln ym ,where g is the underlying long-term balanced growth rate of theeconomy, y is the productivity frontier in manufacturing, ym is
manufacturing labor productivity in the home economy, and (>0) is the convergence coefficient in manufacturing. This formulation captures the basic asymmetry between manufacturing andnonmanufacturing, namely, that manufacturing is the beneficiary of a convergence ‘‘kick,’’ which peters out as the economy
gets closer to the frontier.
Substituting these in and noting that m þ ð1  Þn ¼ 1,
economywide growth becomesð4Þ

y^ ¼ g þ m ðln y  ln ym Þ þ ðm  n Þ d:

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QUARTERLY JOURNAL OF ECONOMICS

So growth equals an exogenous (or country-specific) component, a manufacturing convergence factor (that is decreasing in
the level of manufacturing productivity), and a reallocation term.
The reallocation term captures the effect of changes in the composition of employment across sectors when productivities differ
between manufacturing and nonmanufacturing. In particular,
because m >n in the data, an increase in manufacturing employment share (d) raises growth. I denote the reallocation term as  ðm  n Þ d.
Equation (4) is helpful for understanding why manufacturing convergence does not translate into aggregate convergence.
Most critically, it highlights the role of a, the share of manufacturing employment. The economywide impact of manufacturing
growth is mediated through this variable. One characteristic ofpoor countries is that they have very small formal manufacturingsectors. The average a for the poorest half of the baseline sample
is only around 5%. This means that even powerful convergenceeffects for manufacturing have only tiny consequences for the
economy as a whole. The manufacturing premium m tends tobe larger in poorer countries, but in practice this only partly compensates for the lower a (see later discussion).
In light of this, I would need the reallocation term  to not
only be large but to also vary systematically with incomes, withpoorer countries benefiting substantially more from reallocation
towards manufacturing. However, I fail to find such strong andsystematic reallocation effects in the data.
I illustrate these arguments with a numerical exercise,
which quantifies equation (4). Table VII splits the 1995–2005
sample into 10 deciles according to initial level of aggregate
GDP per worker. I then compute the terms in equation (4) for
each decile. To perform the calculations, I choose values for a,m , and ym that correspond to the averages for each decile. For
, I use .020, which is the value estimated for this sample ofobservations.20 For y* I use the average manufacturing productivity in the top decile of the sample (for manufacturing productivity). The value of g is set equal to 0 with no loss of generality.
For each decile, the table shows the predicted manufacturing
growth rate ðln y  ln ym Þ, the predicted aggregate growthrate due to manufacturing convergence m ðln y  ln ym Þ, and
20. This is different from the estimate noted in Table II because I am working
with the 1995–2005 sample here (and not the baseline sample).

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

199

the total predicted growth rate including the reallocation termm ðln y  ln ym Þ þ .21
For example, for the poorest 10% of the sample, the averagevalues of the parameters are as follows:  ¼ 0:0278 , m ¼ 7:7451,
and ln y  ln ym = 3.5160. As the first row of Table VII shows, thisyields a predicted manufacturing growth rate of 7.1%, an aggregate convergence term of 0.7%, and a predicted overall growthrate that is actually slightly lower at 0.6%.
Table VII shows a steep negative gradient in manufacturing
growth as initial GDP per worker increases (column (4)). This is
as expected from the results presented previously. The predicted
manufacturing growth rate goes down from 7.1% for the bottom
decile to 0.6% at the very top.22 The table also shows that very
little of this gradient survives at the aggregate level once theeffect is scaled down by m , which is substantially smaller
than 1. The aggregate convergence term is a mere 0.7% for thebottom decile (an order of magnitude smaller). The key contributor here is a, which not only is very small but increases as incomesrise, further moderating the forces of convergence (column (1)).
This is only partly offset by the fall in m (column (2)).
Furthermore, the reallocation term is tiny, often negative,and does not vary substantially across income levels to makemuch difference (column (6)). In principle, this term could havemade a substantial contribution to convergence if labor were to
move more rapidly to manufacturing in response to the large
return differentials across sectors. The labor productivity differential between manufacturing and nonmanufacturing ðm  n Þ isof the order of 300–700% for the lowest deciles. With a differentialof 500%, even if a were to increase by 0.5% annually (resulting inroughly a 5 percentage point increase in the employment share ofmanufacturing over a decade), the poorest economies in thesample would experience a growth boost of around 5  0.5 = 2.5
percentage points. As it is, the actual change in a is a minutefraction of that and often negative (as can be observed from the

21. I use end-of-period m and n to compute  because I am dealing with discrete changes.
22. There is a small convergence effect even in the top decile because manufacturing convergence depends on distance from the manufacturing frontier. Table VI
organizes deciles according to GDP per worker, not manufacturing productivity.

0.0278
0.07390.04880.0731
0.09640.07940.13070.14150.13850.15417.7451
3.49134.30413.71422.52522.85622.02181.37681.44401.2064

m

a
7.09%
6.71%
6.14%
5.07%
4.83%
3.67%
3.61%
2.31%
1.42%
0.59%

Manufacturing
growth(3)lny* – lnym
3.51603.32873.04402.51662.39401.8194
1.7922
1.14590.70610.2909

(4)

(3)

0.67%
0.84%
0.42%
1.06%
0.69%
0.62%
0.62%
0.37%
0.17%
0.09%

(5)
Aggregateconvergenceterm(1)(2)(4)0.0016
0.0053
0.0004
0.0005
0.0003
0.0007
0.0003
0.00070.0001
0.0006



(6)

0.51%
0.32%
0.38%
1.01%
0.66%
0.55%
0.60%
0.31%
0.18%
0.03%

(7)
Predicted
aggregate
growth(5) + (6)Notes. Sample: 1995–2005, b = .020, lny* = 11.5098; a, ym, lny* and lnym are beginning-of-period values.  is calculated by multiplying the change in a over the decade with theend-of-period differences in y’s, and dividing the result by 10 to annualize it.

Bottom decile
Decile 9
Decile 8
Decile 7
Decile 6
Decile 5
Decile 4
Decile 3
Decile 2
Top decile

(2)

(1)

TABLE VII
PREDICTED GROWTH RATES BY INCOME (GDP/WORKER) DECILE200
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201

negative entries in column (6)).23 Very similar results are obtained for the panel and baseline samples as well (not shown).
The net result is that the gradient for predicted economywide
growth is barely distinguishable from zero (see column (7)). The
difference between the gradients for manufactures and the aggregate economy is illustrated in Figure VI. In fact, the implied
convergence factor for economywide growth is so small is that it
would be easily swamped by random measurement error or unobserved country-level determinants. For example, the small
negative gradient in Table VII for predicted overall growth becomes statistically indistinguishable from zero if I augment equation (4) with a random error term distributed normally with mean
0 and variance 0.0001 (a standard deviation in unexplained
growth rates across countries of just 1 percentage point).
In sum, aggregate nonconvergence appears to be explainedby the following combination of facts: (1) non-manufacturing
activities do not exhibit unconditional convergence; (2) poor countries have little employment in manufacturing, depressing thecontribution of manufacturing to overall growth; (3) the shareof employment in manufacturing rises over the course of development, giving less-poor countries a growth boost; and (4) thereallocation effect is neither sizable enough nor systematicallylarger at lower income levels. In terms of quantitative magnitudes, the first two factors play the dominant roles. From an economic standpoint, however, the last fact is perhaps mostinteresting, pointing to an important unexploited potential inpoor countries.

V. Concluding Remarks
I have provided evidence in this article that unconditional
convergence is alive and well. One needs to look for it among
manufacturing industries rather than entire economies. It is perhaps not surprising that manufacturing industries should exhibit
unconditional convergence and, if the estimates here are to bebelieved, at quite a rapid pace, too. These industries produce tradable goods and can be rapidly integrated into global production
23. Wong (2006) finds a very small reallocation effect in his study of 13 OECD
countries as well. Wong performs an accounting decomposition for OECD economies, allocating convergence among seven different sectors and an interaction (reallocation) term.

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QUARTERLY JOURNAL OF ECONOMICSFIGURE VIII
Difference between Manufacturing and Economy-wide Growth Gradients.
See text for explanation.

networks, facilitating technology transfer and absorption. Even
when they produce just for the home market, they operate
under competitive threat from efficient suppliers from abroad,requiring that they upgrade their operations and remain efficient. Traditional agriculture, many nontradable services, andespecially informal economic activities do not share thesecharacteristics.
The findings in this article offer new insight on the determinants of economic growth and convergence across countries. Theysuggest that lack of convergence is due not so much to economywide misgovernance or endogenous technological change but tospecific circumstances that influence the speed of structural reallocation from nonconvergence to convergence activities. The
policies that matter are those that bear directly on this reallocation. As discussed in McMillan and Rodrik (2011) and Rodrik(2011b), what high-growth countries typically have in common
is their ability to deploy policies that compensate for themarket and government failures that block growth-enhancing

UNCONDITIONAL CONVERGENCE IN MANUFACTURING

203

structural transformation. Countries that manage to affect therequisite structural change grow rapidly, and those that fail
don’t.
Put differently, successful countries experience both productivity convergence in formal manufacturing and rapid industrialization. Unsuccessful countries make do with just the former.

Supplementary Material
An Online Appendix for this article can be found at QJE
online (qje.oxfordjournals.org).
John F. Kennedy School of Government, Harvard
University

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